1,745 research outputs found
Strategic Intelligence Monitor on Personal Health Systems (SIMPHS): Report on Typology/Segmentation of the PHS Market
This market segmentation reports for Personal Health Systems (PHS) describes the methodological background and illustrates the principles of classification and typology regarding different fragments forming this market. It discusses different aspects of the market for PHS and highlights challenges towards a stringent and clear-cut typology or defining market segmentation. Based on these findings a preliminary hybrid typology and indications and insights are created in order to be used in the continuation of the SIMPHS project. It concludes with an annex containing examples and cases studies.JRC.DDG.J.4-Information Societ
Telemedicine Scenario for Elderly People with Comorbidity
Progressive population aging is associated with negative social and economic impacts mainly due to its associated comorbidity rather than to aging per se. In this regard, information and communication technology resources may provide useful tools to assist the population with comorbidities through the use of telemedicine systems. However, despite their potential, such systems have not yet been effectively implemented due to a number of different reasons: absence of a clear business plan, poor acknowledgement of their clinical usefulness, and ethical and legal issues, among others. An analysis of current scenario from the point of view of the different actors (patients, health care providers, and health care systems) aimed at identifying the needs to be covered by telemedicine systems that could contribute to overcoming such problems. The present chapter is intended to offer such an analysisPostprint (author’s final draft
A Service-oriented Architecture for Ambient-Assisted Living
Ambient-Assisted Living (AAL) is currently an important research and development area, mainly due to the rapidly aging society, the increasing cost of health care, and the growing importance that individuals place on living independently. The general goal of AAL solutions is to apply ambient-assisted intelligence to enable people with specific demands (e.g. handicapped or elderly) to live in their preferred environment longer by tools (i.e. smart objects, mobile and wearable sensors, intelligent devices) being sensitive and responsive to the presence of people and their actions. The research describes the design and development of a novel service-oriented system architecture where different smart objects and sensors are combined to offer ambient-assisted living intelligence to older people. The design stage is driven by a user-centred approach to define an interoperable architecture and human-oriented principles to create usable products and well-accepted services. Such architecture has been realized in the context of an Italian research project funded by the Marche Region and promoted by INRCA (National Institute on Health and Science of Aging) in the framework of smart home for active ageing and ambient assisted living. The result is an interoperable and flexible platform that allows creating user-centred services for independent living
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
- …